Bayesian Model

From the retina to action: Dynamics of predictive processing in the visual system
Within the central nervous system, visual areas are essential in transforming the raw luminous signal into a representation which …
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From the retina to action: Dynamics of predictive processing in the visual system
Bayesian Modeling of Motion Perception using Dynamical Stochastic Textures
A common practice to account for psychophysical biases in vision is to frame them as consequences of a dynamic process relying on …
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Bayesian Modeling of Motion Perception using Dynamical Stochastic Textures
The flash-lag effect as a motion-based predictive shift
Due to its inherent neural delays, the visual system has an outdated access to sensory information about the current position of moving …
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The flash-lag effect as a motion-based predictive shift
Estimating and anticipating a dynamic probabilistic bias in visual motion direction
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Anticipating a moving target: role of vision and reinforcement
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Eye tracking a self-moved target with complex hand-target dynamics
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Active inference, eye movements and oculomotor delays
This paper considers the problem of sensorimotor delays in the optimal control of (smooth) eye movements under uncertainty. …
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Active inference, eye movements and oculomotor delays
Signature of an anticipatory response in area V1 as modeled by a probabilistic model and a spiking neural network
As it is confronted to inherent neural delays, how does the visual system create a coherent representation of a rapidly changing …
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Signature of an anticipatory response in area V1 as modeled by a probabilistic model and a spiking neural network
2014-04-25
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Axonal delays and on-time control of eye movements
Moving objects generate sensory information that may be noisy and ambiguous, yet it is important to be able to reconstruct object speed …
2014-01-10
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Anisotropic connectivity implements motion-based prediction in a spiking neural network
Predictive coding hypothesizes that the brain explicitly infers upcoming sensory input to establish a coherent representation of the …
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Motion-based prediction and development of the response to an 'on the way' stimulus
Smooth Pursuit and Visual Occlusion: Active Inference and Oculomotor Control in Schizophrenia
This paper introduces a model of oculomotor control during the smooth pursuit of occluded visual targets. This model is based upon …
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Grabbing, tracking and sniffing as models for motion detection and eye movements
Moving objects generate sensory information that may be noisy and ambiguous, yet it is important to be able to reconstruct object speed …
2012-01-27
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Motion-based prediction is sufficient to solve the aperture problem
In low-level sensory systems, it is still unclear how the noisy information collected locally by neurons may give rise to a coherent …
2012-01-12
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Motion-based prediction is sufficient to solve the aperture problem
In low-level sensory systems, it is still unclear how the noisy information collected locally by neurons may give rise to a coherent …
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Motion-based prediction is sufficient to solve the aperture problem
In low-level sensory systems, it is still unclear how the noisy information collected locally by neurons may give rise to a coherent …
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Motion-based prediction is sufficient to solve the aperture problem
Perceptions as Hypotheses: Saccades as Experiments
If perception corresponds to hypothesis testing (Gregory, 1980); then visual searches might be construed as experiments that generate …
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Perceptions as Hypotheses: Saccades as Experiments
Role of motion-based prediction in motion extrapolation
Propriétés émergentes d'un modèle de prédiction probabiliste utilisant un champ neural
Sensory informations such as visual images are inherently variable. We use probabilistic models to describe how the low-level visual …
2011-07-02
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Pursuing motion illusions: a realistic oculomotor framework for Bayesian inference
Accuracy in estimating an object’s global motion over time is not only affected by the noise in visual motion information but …
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Role of motion inertia in dynamic motion integration for smooth pursuit
Probabilistic models of the low-level visual system: the role of prediction in detecting motion
Sensory informations such as visual images are inherently variable. We use probabilistic models to describe how the low-level visual …
2010-12-17
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Models of low-level vision: linking probabilistic models and neural masses
2010-01-08
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A recurrent Bayesian model of dynamic motion integration for smooth pursuit
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Dynamical emergence of a neural solution for motion integration
Dynamical emergence of a neural solution for motion integration
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Probabilistic models of the low-level visual system: the role of prediction in detecting motion
Sensory informations such as visual images are inherently variable. We use probabilistic models to describe how the low-level visual …
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Decoding center-surround interactions in population of neurons for the ocular following response
Short presentation of a large moving pattern elicits an Ocular Following Response (OFR) that exhibits many of the properties attributed …
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Dynamics of distributed 1D and 2D motion representations for short-latency ocular following
Integrating information is essential to measure the physical 2D motion of a surface from both ambiguous local 1D motion of its …
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Dynamics of distributed 1D and 2D motion representations for short-latency ocular following
Decoding the population dynamics underlying ocular following response using a probabilistic framework
The machinery behind the visual perception of motion and the subsequent sensorimotor transformation, such as in Ocular Following …
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Decoding the population dynamics underlying ocular following response using a probabilistic framework
Modeling spatial integration in the ocular following response to center-surround stimulation using a probabilistic framework
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What adaptive code for efficient spiking representations? A model for the formation of receptive fields of simple cells
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What adaptive code for efficient spiking representations? A model for the formation of receptive fields of simple cells
Dynamical Neural Networks: modeling low-level vision at short latencies
The machinery behind the visual perception of motion and the subsequent sensori-motor transformation, such as in ocular following …
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Bayesian modeling of dynamic motion integration
The quality of the representation of an object’s motion is limited by the noise in the sensory input as well as by an intrinsic …
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Modeling spatial integration in the ocular following response using a probabilistic framework
The machinery behind the visual perception of motion and the subsequent sensori-motor transformation, such as in Ocular Following …
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Modeling spatial integration in the ocular following response using a probabilistic framework
Visual tracking of ambiguous moving objects: A recursive Bayesian model
Perceptual and oculomotor data demonstrate that, when the visual information about an object’s motion differs on the local …
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Bayesian modeling of dynamic motion integration
The quality of the representation of an object’s motion is limited by the noise in the sensory input as well as by an intrinsic …
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Input-output transformation in the visuo-oculomotor loop: modeling the ocular following response to center-surround stimulation in a probabilistic framework
The quality of the representation of an object’s motion is limited by the noise in the sensory input as well as by an intrinsic …
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Input-output transformation in the visuo-oculomotor loop: modeling the ocular following response to center-surround stimulation in a probabilistic framework
The quality of the representation of an object’s motion is limited by the noise in the sensory input as well as by an intrinsic …
2006-01-01
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Dynamics of motion representation in short-latency ocular following: A two-pathways Bayesian model
The integration of information is essential to measure the exact 2D motion of a surface from both local ambiguous 1D motion produced by …
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Feature detection using spikes : the greedy approach
A goal of low-level neural processes is to build an efficient code extracting the relevant information from the sensory input. It is …
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Feature detection using spikes : the greedy approach